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GitHub - lechmazur/position_bias: A benchmark for testing whether LLM judges keep the same preference when two lightly edited versions of the same story are shown in opposite orders. Flex routing (EU and EFTA) Dark Factories: Retooling for LLM Velocity Ask HN: What would be the impact of a LLM output injection attack? GitHub - Oaklight/llm-rosetta: Production-ready LLM API translation layer for Python — bidirectional conversion between OpenAI, Anthropic & Google formats via hub-and-spoke IR. Optional API gateway. Streaming & non-streaming. Zero core deps. Contributions welcome! GitHub - browser-use/browser-harness: Self-healing browser harness that enables LLMs to complete any task. GitHub - moeen-mahmud/remen: Remen turns thoughts into something you can return to Analyzing 156 LLM Launch Posts on Hacker News ChatGPT vs Gemini vs Claude: The Best LLM Subscription You Should Buy GitHub - salaamalykum/quran-semantic-search: High-density RAG Semantic Search Engine & Quran Corpus (GEO/SEO Architecture) GitHub - NVIDIA/TensorRT-LLM: TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way. The State of LLM Bug Bounties in 2026 Operational Readiness Criteria for Tool-Using LLM Agents Meshcore: Architecture for a Decentralized P2P LLM Inference Network How an LLM becomes more coherent as we train it GitHub - seetrex-ai/laimark GitHub - Jossifresben/BibCrit: AI-assited biblical textual criticism GitHub - wastedcode/memex: File system based wiki, maintained by Claude 99helpers.com GitHub - cliver-project/AITrigram GitHub - unbody-io/adapt: A self-evolving memory layer for AI agents. GitHub - hb20007/awesome-gen-ai-fails: A list of incidents where reliance on generative AI and LLMs resulted in harm to companies, individuals, or society GitHub - nevenkordic/localmind: Run any local LLM with persistent memory and context. CLI agent over Ollama with SQLite-backed hybrid recall. No cloud. Ask HN: What are the machine requirements for a LLM like Llama-3.1-8B? Faster LLM Inference via Sequential Monte Carlo grpo explained: group relative policy optimization for llm finetuning - cgft Stop comparing price per million tokens: the hidden LLM API costs · TensorZero Andrej Karpathy's LLM Wiki Is a Bad Idea GitHub - GG-QandV/mnemostroma: Offline RAM-first cognitive leer/coprocessor for AI agents and robotics. Solves "Context Abandonment" with 20-80ms latency using a dual-thread biomimetic memory architecture (ONNX + SQLite WAL). mempalace/agent at agent · skorotkiewicz/mempalace
Stop Paying Analysts to Search the Internet | Quoin.ai
quoinai · 2026-06-25 · via Hacker News - Newest: "LLM"

Research Engine · Not a Chatbot

Decision-grade research. In 20 minutes.

Quoin delivers sourced, cited, primary-document research in 20 minutes. The kind of output you can defend in a partner meeting, a regulatory review, or a board room. Not a summary. Not a chatbot. Research.

What 20 minutes buys you

Your analysts didn't go to school to search the internet.

Quoin handles the searching, verifying, and structuring, fully cited, primary-source-backed, and ready in 20 minutes. What's left for your team is the judgment you actually hired them for.

200 companies. One portfolio. Still just 20 minutes.

Portfolio reviews shouldn't take weeks. With Quoin, upload your full list of portfolio companies and get decision-grade research on every single one, simultaneously. Whether you're covering 1 company or 200, Quoin delivers the same depth in the same 20 minutes.

When the research is complete, ask Claude to synthesize everything into a single executive summary. Upload additional context, such as your own notes, prior reports, or internal data, and Quoin folds it all in. Your quarterly review just got a lot shorter.

User story

Who
A fund manager, venture investor, family office principal, or portfolio analyst responsible for quarterly reporting.

The challenge
A quarterly portfolio review requires fresh, structured research on every company in the portfolio. For a 20-company portfolio, that's 80 to 160 hours of manual work, every single quarter, before a single slide is written.

With Quoin
Upload your portfolio company list to Claude and instruct it to run your custom Quoin agent against each company. In 20 minutes, you have a fresh stack of decision-grade research on every portfolio company, simultaneously. Ask Claude to synthesize the research into a board report or LP update, and enhance it with your own financials, board decks, or prior transcripts for full context.

The result
What used to take weeks now takes about 30 minutes, start to finish. Every insight is sourced. Every quarter.

200 companies. One portfolio. Still just 20 minutes.

Portfolio reviews shouldn't take weeks. With Quoin, upload your full list of portfolio companies and get decision-grade research on every single one, simultaneously. Whether you're covering 1 company or 200, Quoin delivers the same depth in the same 20 minutes.

When the research is complete, ask Claude to synthesize everything into a single executive summary. Upload additional context, such as your own notes, prior reports, or internal data, and Quoin folds it all in. Your quarterly review just got a lot shorter.

User story

Who
A fund manager, venture investor, family office principal, or portfolio analyst responsible for quarterly reporting.

The challenge
A quarterly portfolio review requires fresh, structured research on every company in the portfolio. For a 20-company portfolio, that's 80 to 160 hours of manual work, every single quarter, before a single slide is written.

With Quoin
Upload your portfolio company list to Claude and instruct it to run your custom Quoin agent against each company. In 20 minutes, you have a fresh stack of decision-grade research on every portfolio company, simultaneously. Ask Claude to synthesize the research into a board report or LP update, and enhance it with your own financials, board decks, or prior transcripts for full context.

The result
What used to take weeks now takes about 30 minutes, start to finish. Every insight is sourced. Every quarter.

AI tools generate. Quoin investigates.

Every claim sourced

Every statement in a Quoin report links back to a primary document: filings, press releases, databases, court records.

No hallucinations

Quoin doesn't generate plausible-sounding facts. It finds real ones and shows its work.

Hundreds of pages in 20 minutes

The output isn't a paragraph. It's a full research document, the kind that used to take a team of analysts days to produce.

Why Quoin

Research built for decisions

Most tools generate text. Quoin generates evidence. Here's how the approaches compare.

Quoin

Agentic AI

Decision-grade intelligence. Every claim atomized, sourced, and mechanism-anchored. Defensible in any room where being wrong has consequences.

  • Reads thousands of live primary sources in minutes
  • Every claim traced to a primary document (no hallucinations)
  • Structured output: claim, source, mechanism, timestamp, confidence, bounds
  • Always current, verified against live web and documents
  • Scales across any business, topic, or industry

Time to complete~20 minutes

Primary sources read1,000s per brief

Hallucination riskNone

Manual

What serious analysts have produced for a century: credentialed and sourced, but constrained by human bandwidth.

  • Hours or days to produce a single research brief
  • Limited to what one analyst can read and retain
  • Cognitive bias and fatigue affect quality and consistency
  • Expensive to produce at the depth decisions require
  • Does not scale; every new question starts from scratch

Time to complete5 to 20 hours

Sources read5 to 20 sources

Output defensibilityVaries by analyst

Frontier AI

Consumer-grade grounding. Fast at generating text. Unable to produce output that survives a serious decision.

  • Produces plausible claims with no primary source behind them
  • Cannot trace a claim to an actual filing, contract, or document
  • Knowledge frozen at the training cutoff, not refreshed from the live web
  • No mechanism, no confidence grade, no stated bounds
  • Will not survive a partner meeting or a regulator's review

Time to complete~1 min

Primary sources readNone

Hallucination riskHigh

That's not a research problem. That's a tax on every decision your organization makes.

Whether you're running investment diligence, competitive research, or sales preparation, Quoin returns a defensible, fully cited document in the time it takes to grab lunch. Hours spent hunting for information you're not sure you can trust, stitching together sources you'll have to verify anyway, and producing output that would be twice as good if you just had twice as much time. Quoin exists for anyone whose work demands a defensible answer, whether you're in a partner meeting, a discovery call, a briefing room, or a university library. If you need to know things, and being wrong has consequences, you've been doing this the hard way. You don't have to anymore.

Guardrails

No agent ever checks its own work.

Quoin is built on MARCH - a verification architecture where the agents that generate research and the agents that check it are completely separate systems. They don't share context. They can't influence each other's conclusions. Every claim is found, then independently verified, then traced to a primary source before it appears in your report. That's not a feature. It's the architecture.

1

Research scoping

Structured intake translates your question into a research plan, defining scope, source requirements, and the guardrails each agent will be held to.

2

Specialized generator agents

Domain-specific agents research filings, extract narratives, benchmark peers, and surface anomalies, each optimized for a specific task, none responsible for checking what another produced.

3

Isolated verification layer

Independent verifier agents check every output against primary sources, without access to the generator's reasoning chain. No shared context means no confirmation bias. What gets flagged is what a single model would have missed.

4

Reconciled, citation-backed delivery

Contradictions are resolved before anything reaches you. Outputs are structured as research briefs, credit memos, or analyst reports, with every claim traceable to the source that verified it.

Get started

Ready to transform your research?

Join organizations using Quoin to streamline research and make data-driven decisions with confidence.